3 papers
cs.AR2025
SnipSnap: A Joint Compression Format and Dataflow Co-Optimization Framework for Efficient Sparse LLM Accelerator Design
Junyi Wu, Chao Fang, Zhongfeng Wang
The growing scale of large language models (LLMs) has intensified demands on computation and memory, making efficient inference a key challenge. While sparsity can reduce these cos…
cs.AR2025
Enable Lightweight and Precision-Scalable Posit/IEEE-754 Arithmetic in RISC-V Cores for Transprecision Computing
Qiong Li, Chao Fang, Longwei Huang +2
While posit format offers superior dynamic range and accuracy for transprecision computing, its adoption in RISC-V processors is hindered by the lack of a unified solution for ligh…
cs.AR2024
Anda: Unlocking Efficient LLM Inference with a Variable-Length Grouped Activation Data Format
Chao Fang, Man Shi, Robin Geens +3
The widely-used, weight-only quantized large language models (LLMs), which leverage low-bit integer (INT) weights and retain floating-point (FP) activations, reduce storage require…